Binary image processing techniques are often used to reduce the computational burden associated with image processing algorithms, thereby reducing costs and increasing throughput. When applicable, this approach is particularly useful for object detection, for which grey-level processing is quite slow. In this paper, a novel algorithm for detecting objects in binary images is presented. The fundamental advantage of this algorithm is the use of binary connectivity edge maps, which represent the edge data in the binary image by detecting eight-connected neighborhood bit (intensity) patterns. Appropriate use of these connectivity edge maps yields object detection which is faster and more robust than traditional methods (image subtraction, image correlation). The efficacy of this algorithm is demonstrated by applying it to the automatic detection and location of alignment marks for semiconductor wafer alignment and directly comparing its performance against those of traditional approaches (image subtraction, image correlation) in terms of accuracy, invariance to object rotation, and algorithm speed.